Journal article
Deep Bayesian Gaussian processes for uncertainty estimation in electronic health records
- Abstract:
- One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to model predictions. Currently, deep Bayesian neural networks and sparse Gaussian processes are the main two scalable uncertainty estimation methods. However, deep Bayesian neural networks suffer from lack of expressiveness, and more expressive models such as deep kernel learning, which is an extension of sparse Gaussian process, captures only the uncertainty from the higher-level latent space. Therefore, the deep learning model under it lacks interpretability and ignores uncertainty from the raw data. In this paper, we merge features of the deep Bayesian learning framework with deep kernel learning to leverage the strengths of both methods for a more comprehensive uncertainty estimation. Through a series of experiments on predicting the first incidence of heart failure, diabetes and depression applied to large-scale electronic medical records, we demonstrate that our method is better at capturing uncertainty than both Gaussian processes and deep Bayesian neural networks in terms of indicating data insufficiency and identifying misclassifications, with a comparable generalization performance. Furthermore, by assessing the accuracy and area under the receiver operating characteristic curve over the predictive probability, we show that our method is less susceptible to making overconfident predictions, especially for the minority class in imbalanced datasets. Finally, we demonstrate how uncertainty information derived by the model can inform risk factor analysis towards model interpretability.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 3.1MB, Terms of use)
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- Publisher copy:
- 10.1038/s41598-021-00144-6
Authors
- Publisher:
- Springer Nature
- Journal:
- Scientific Reports More from this journal
- Volume:
- 11
- Issue:
- 1
- Article number:
- 20685
- Publication date:
- 2021-10-19
- DOI:
- EISSN:
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2045-2322
- Pmid:
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34667200
- Language:
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English
- Keywords:
- Pubs id:
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1206034
- Local pid:
-
pubs:1206034
- Deposit date:
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2021-11-03
Terms of use
- Copyright holder:
- Li et al.
- Copyright date:
- 2021
- Rights statement:
- © The Author(s) 2021. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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